Lakhmissi Cherroun
Papers
12
Total Citations
103
H-Index
6
About
Lakhmissi Cherroun is a leading researcher in autonomous mobile robotics, specializing in intelligent navigation, path planning, and control systems. His work centers on integrating soft computing techniques—particularly fuzzy logic, neural networks, and neuro-fuzzy controllers—to enable robots to navigate complex, unpredictable environments. Cherroun’s major contributions include developing optimized fuzzy logic controllers for path planning and visual navigation, as demonstrated in his highly cited 2019 paper on mobile robot visual navigation using fuzzy logic and optical flow approaches (23 citations). He has also pioneered comparative studies of type-1 and type-2 fuzzy logic controllers for autonomous robotic motion, advancing robust goal-seeking and obstacle-avoidance behaviors. His research extends to reinforcement learning, flood-fill algorithms for maze navigation, and optical flow-based visual structures using Lucas-Kanade and Horn-Schunck algorithms. With over a decade of publications, Cherroun’s work has garnered significant impact, including papers cited 18, 11, and 10 times. His notable achievements include proposing novel neuro-fuzzy controllers for path following and systematically comparing fuzzy, neural, and neuro-fuzzy approaches for mobile robot path tracking. Cherroun’s research continues to shape the field of intelligent autonomous systems, offering practical solutions for real-world robotic navigation challenges.
Research Focus
Key Achievements
Top Papers
- 1
- 2Mobile Robot Path Planning Based on Optimized Fuzzy Logic Controllers18 citations · 2019
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- 4Type-1 and Type-2 Fuzzy Logic Controllers for Autonomous Robotic Motion10 citations · 2019
- 5
- 6Type-1 and Type-2 Fuzzy Techniques: Application to Robotic Systems6 citations · 2023
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- 10